Scaling a Video-Based Identification Platform

This project involved transforming a video-based identification platform that was experiencing critical production issues during peak usage periods into a reliable, scalable system.

The Challenge

The platform was handling critical identification processes, but downtime and performance issues were impacting service availability. The system lacked proper observability to diagnose problems quickly, and scaling mechanisms were not effectively managing peak loads.

Our Solution

As Site Reliability Engineers, we focused on three core pillars:

1. Production Stability

We implemented comprehensive monitoring and alerting systems using Prometheus and Grafana, providing visibility into every layer of the application. By establishing clear SLIs and SLOs, we could proactively address issues before they became critical incidents.

2. Infrastructure Scalability

We containerized the application using Docker and orchestrated it with Kubernetes, enabling horizontal scaling based on demand. The infrastructure now automatically adjusts to handle traffic spikes without manual intervention.

3. Observability and Reliability

By implementing distributed tracing and structured logging, we reduced mean time to resolution (MTTR) significantly. The platform can now handle 10x the previous peak load while maintaining 99.9% uptime.

Results

• 99.9% uptime achieved during peak usage periods
• 10x capacity increase with automatic scaling
• 80% reduction in incident response time
• Zero critical incidents since implementation

The platform is now production-ready and can scale seamlessly to meet growing demand.